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Found 10 papers, 2 papers with code
DiT-Head: High-Resolution Talking Head Synthesis using Diffusion Transformers
We propose a novel talking head synthesis pipeline called "DiT-Head", which is based on diffusion transformers and uses audio as a condition to drive the denoising process of a diffusion model.
Cognitively Inspired Components for Social Conversational Agents
Current conversational agents (CA) have seen improvement in conversational quality in recent years due to the influence of large language models (LLMs) like GPT3.
Algebras of actions in an agent's representations of the world
In this paper, we propose a framework to extract the algebra of the transformations of worlds from the perspective of an agent.
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning
To replace data augmentation, this paper proposed a method called SLAP to intensify experience to speed up machine learning and reduce the sample size.
Efficient entity-based reinforcement learning
Recent deep reinforcement learning (DRL) successes rely on end-to-end learning from fixed-size observational inputs (e. g. image, state-variables).
AIGenC: An AI generalisation model via creativity
Inspired by cognitive theories of creativity, this paper introduces a computational model (AIGenC) that lays down the necessary components to enable artificial agents to learn, use and generate transferable representations.
Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks
In this paper we introduce Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from Faster R-CNN for logo generation.
Gaussian Process Regression for Probabilistic Short-term Solar Output Forecast
First, we categorise the data into four groups based on solar output and time by using k-means clustering.
Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models
Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality.
Towards Symbolic Reinforcement Learning with Common Sense
Deep Reinforcement Learning (deep RL) has made several breakthroughs in recent years in applications ranging from complex control tasks in unmanned vehicles to game playing.
